This article shares a personal Behavioral Economics journey. It is also a historical account and shows exciting applications for the future. Discussed are the field's amazing scientific breadth, its short and interesting history, the field's key influencers, some exciting commercial and non-profit applications, and how math is applied to help people and organizations make great decisions.
First. let's start with my elevator pitch answer to the question - "What is Behavioral Economics?":
"I help people make the best decisions by applying a consistent, repeatable decision process."
then, if the elevator ride goes a few more floors...
"I do this by helping them apply their own judgments, curate their data, and by building their confidence. This helps people get the biggest bang for their buck when making important life decisions."
It is that simple! Well, not really - please read on for a more becoming answer.
About the author: Jeff Hulett is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM. Today, Jeff is an executive with the Definitive Companies. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his new book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions -- at jeffhulett.com.
Becoming Behavioral Economics -- When I started in economics, the name "behavioral economics" did not yet exist. Next is a brief story of how my experience aligns with the birth of this economics discipline. Like many evolving disciplines, there is no single starting point. It is more of a recombining of existing disciplines, discarding past misunderstandings, and implementing new insights that revealed Behavioral Economics. It did not happen all at once.
I have academic degrees in math, finance, and economics. Early on, as an undergraduate, I figured finance was a good route for a banking career. Plus, if banking did not work out, at least I would have practical skills -- like balancing a checkbook. I came to realize that data was going to be a big thing. This was in the late 1980s, so it was not yet so obvious. At that point, the internet was mostly used by government and research universities for electronic file transfer. My computer in grad school was an "8088" that I built myself. It barely had enough memory to hold a simple spreadsheet. By today's standards, it was like I was using an abacus! And yes, in case you were wondering, I was really good at DOS commands. But even though the computing power and data bandwidth were not YET at the tipping point, the trends seemed obvious that data and data technology were poised to explode. It was a matter of WHEN not IF.
That is when I fell upon economics. Economics is found at a sweet spot between finance, data, and human behavior. It also has a helpful mathematical rigor. So this started me on the path toward behavioral economics.... but I was not there yet.
Relatively early in my banking career, I had a "Red Pill" moment of epiphany. If you ever saw the movie "The Matrix" with Keanu Reeves, you will know what I am talking about. This is when my behavioral economics passion rocket launched.
Before the Red Pill: When I was in grad school, economics was taught in the Paul Samuelson-impacted neoclassical economics framework. Dr. Samuelson "mathematized" economics in the 1950s based on his book called The Foundations of Economic Analysis. The math was great, the economic way of thinking was great, but there was a problem hidden in the underlying assumptions. To make the math work, economists needed to assume that there was a single, rational outcome to an economic problem. That is, old-school economics assumed there is a single "rational" point upon which all people converged. Anyone who is a human knows that this behavioral assumption is pretty silly. I call this flawed, old-school definition of rationality - "robo-rationality."
My red pill moment was finding the very new and growing behavioral economics movement initiated by people like Daniel Kahneman and Richard Thaler. Behavioral economics was answering a question I had just begun to learn how to ask. Kahneman's Nobel-winning work, written in 1979, is called Prospect Theory. Kahneman, Thaler, and several pioneering behavioral economists overlaid behavioral psychology on the existing economics framework. It helped that Kahneman was an experienced psychologist. Behavioral psychology helped pivot the economic framework so that it makes sense for how people actually think. Behavioral psychology is supported by the fast-growing neurobiology field. Neurobiology helps us understand how the brain works in terms of why the brain's psychology presents as it does. Neurobiological building blocks include:
a) neurons, synapses, neurotransmitters, and our expansive neuroplasticity plus -
b) how the staggering volume of our brain matter interacts.
These building blocks are foundational for why our psychology-based economic behavior sometimes presents in unexpected or inconsistent ways.
Behavioral economics recognizes people are people, instead of assuming we are rational robots. Human psychology is VERY quirky. We have high-impact cognitive biases central to why singular robo-rationality does not work. As a result, "rationality" itself was redefined. In the behavioral economics school, the redefined "diverse rationality" varies by each person. It is impacted by situational framing, anchoring, and related cognitive biases. Robo-rationality is only a boundary condition for a varying, multimodal set of diverse rationality locations found in the total rationality space. These locations are subject to change over time and condition. Thus, what is perfectly rational to you is not necessarily rational for me. Diverse rationality is better understood as user-defined assessments, based on varying individual perceptions made with an imperfect and incomplete set of data. As the literature suggests, in certain cases, one's diverse rationality may persist for long time periods. Â [i]
Decisions under uncertainty are difficult for most people. Our cognitive biases impact how people perceive and make decisions under uncertainty. Via evolution and an environment that changes faster than our genome, cognitive biases are not always helpful decision shortcuts in the modern age. Behavioral economics helps us understand how people will make decisions based on uncertain time and condition factors unique to individual decision-makers. Behavioral economics is not for deciding "what is rational," it is about helping people and organizations to adapt as the causes and impacts of our diverse rationality are revealed.
Allowing for diverse rationality in the economics context opens amazing possibilities, some of which are presented next. Interestingly, Samuelson won a Nobel Prize for implementing the well-intended but flawed version of robo-rationality. It took both Kahneman and Thaler winning Nobel Prizes of their own to unwind the flaw! Ironically, cognitive biases associated with inertia and groupthink can even impact the Nobel committee and the broader academic community. [ii]
Another essential part of behavioral economics is that it is VERY TESTABLE. The work of Samuelson was more based on convenient, theoretical deductions about how people ought to behave. With the explosion in data and data management technologies, induction-based Randomized Control Trials ("RCTs") are performed to test behavioral theories. These are congruent to RCTs done in the hard sciences. RCTs are the gold standard for determining causality. Causality is at the core of the scientific method. Also, behavioral economists have improved their ability to detect and measure natural experiments. This is helpful as sometimes social science-enabling control groups are challenging to construct "in the wild."
Behavioral economists are getting better at scaling conclusions from RCTs. For example, Richard Thaler is famous for his work on retirement savings. His behavioral economics-inspired program, called "Save For Tomorrow," is one of the most successful, large-scale programs to help increase savings rates in the United States. Thaler, with the help of many others, "crossed the chasm" to widely implement his good idea from tested nudges. In my days at banks like Citibank, Capital One, and Wells Fargo, we used RCTs extensively to determine how to help people get credit and pay their loan bills. Similarly, we faced the familiar challenges of how to implement our RCT-based consumer banking insights across the banking enterprise. Thus, achieving behavioral economics-based insights was only part of the challenge. Building culture and capability to both update and scale those insights in the enterprise is an essential, next-level challenge. Rapid insight updating and the structure and efficiency necessary for scale have a natural tension. It is a cultural tightrope.
Banking and other data-intensive consumer product companies have a great opportunity to leverage behavioral economics. A very useful approach is the behavioral economics-focused "scale units" found in a growing number of progressive enterprises. Many banking organizations and consumer-focused companies use some form of the scale unit. I was fortunate to help lead the handful of banking organizations mentioned earlier. My friend and behavioral economist John List has led scale unit-like economic organizations for Lyft, Uber, and Walmart.
The scale unit is:
An analytically focused organization that performs behavioral and automation-based testing and analytics. It applies scientific principles in the service of causality. The scale unit is a flywheel enabling long-term company success.
The scale unit's benefits include:
It helps enterprises understand the economic value of business activities intended to drive business success.
It recommends decisions to optimize risk and improve customer delight.
It results in long-term growth, profitability, and scale.
A well-functioning scale unit is a cultural enabler for creativity and ongoing adaptability.
For a scale unit deeper dive, including scale unit success behaviors, check out the article:
The scale unit flywheel
Framework to integrate economics into your organization
Today, I am an executive with the Choice Architecture firm, Definitive. Choice architecture is a technology that implements behavioral economics' learnings in a way that helps people or organizations make the best decisions for them. In many ways, personal choice architecture is a countermeasure for the big consumer platforms that seek to convince you to buy things that may violate your personal utility. Choice architecture has 3 big benefits called DECISION A-C-T.
A – it is accelerated because the best decision process is nimble and engaging.
C - it is confidence-inspired because the best decision process provides us with the confidence to move forward, and then finally
T – it is transparency-enabled because it gives you reports, and you can share with others on your decision team.
Math is the underpinning for behavioral economics science and technology. Certainly, intuitive and theoretical math like differential calculus is very helpful. Also, some of the mathematics of physics like entropy and ergodicity are also useful. The underpinnings of finance are found in the mathematics of convexity and the time value of money. Convexity mathematics relates to our brain's natural challenge to handle non-linear growth and the difference between risk and ruin. The math is all related.
RCTs are formed via the use of statistics. Statistical analysis helps us compare test and control groups, determine the strength of a hypothesis, and scale findings beyond our test work. Statistics is at the core of Artificial Intelligence sometimes used to assist with the test work. Bayesian inference and conditional probability are at the core of updating beliefs. Most choice architecture utilizes some form of Bayesian inference. [iii]
A very practical math is linear algebra, eigenvectors, and eigenvalues. This is the math used to specify individual utility functions. That is, what is important to YOU about your purchases? The utility functions are developed using a technique called the Analytical Hierarchy Process ("AHP") developed by Thomas Saaty in the 1970s. It is still used today! In addition, we use math from the traditional constrained optimization mechanics of linear and non-linear programming.
Finally, part of my personal mission is to spread the massive benefits of behavioral economics in the Personal Finance domain. Today, we have a significant financial literacy challenge in our country. Through the use of choice architecture and evidence-based personal finance research, I wrote the book Making Choices, Making Money: Your Guide to Making Confident Financial Decisions. I teach personal finance at James Madison University. I also lead a non-profit, called Definitive Social, to provide choice architecture to those people who can really use it.
So, this is my answer to "What is Behavioral Economics?" Since it is a relatively young field, I am sure other economists have a different experience. The point is, as we learn about our neurobiology, our psychology, and how it impacts the economics of our everyday lives, the field will evolve to help overcome a diverse array of challenges. It is a very exciting time and it will only get more exciting!
Notes
[i] "The persistence of diverse rationality" cases are where immediate modest payoffs crowd out needed investment to achieve much greater long-term benefits. Next are examples provided by Korteling, et al, 2023 -
"For example, it often seems to underestimate the long-term dangers of things like global warming and species extinction. This can make even major future threats seem insufficient motivation for determined action (Berger, 2009). In general, we see these types of typical, and often flawed, decision making patterns in many different contexts of our society (Eigenauer, 2018). For instance, Flyvbjerg (2009) showed that 9 out of 10 transportation infrastructure projects end up in large cost overrun, which did not improve over time, even over a period of 70 years. Other examples of persisting problems that for a major part follow from poor decision making are: improper and incorrect diagnoses as well as harmful patient decisions in medicine and health care (Croskerry, 2003; Groopman, 2007); overly optimistic growth assessments and ill-advised lending policies in global finance (Shiller, 2015); optimistic decision making in personal finance, like susceptibility to scams (Modic and Lea, 2013); against all knowledge continue a chosen course or investment with negative outcomes rather than alter it (Arkes and Blumer, 1985; Garland and Newport, 1991); perpetuating injustice through personal prejudice and unjust sentencing (Benforado, 2015)"
[ii] Even Kahneman and Thaler cannot claim to be the originator of behavioral economics. Much of the generalized behavioral economics framework was specified by Adam Smith in his book The Theory of Moral Sentiment, published in 1759. The interactions of a varying set of potential rationalities across people were theorized as the "invisible hand" and operationalized by Smith's framework: "The Four Sources of Moral Approval."
Kahneman and Thaler steered economics back to the more holistic Adam Smith road after Samuelson created a well-intended but less-than-accurate mathematical offramp.
Adam Smith defined the invisible hand as the mechanism by which all our preference bundles, via the market process, dynamically interact to settle on a single market price equilibrium. He also defined the "Impartial Spectator" as an imagined aggregation of all those that you respect and want to do well by. The impartial spectator is like your 'conscience person' by which you compare and evaluate all your actions. While Smith does not explicitly relate the impartial spectator to God or some deity, you may interpret the impartial spectator as such. Smith wisely leaves it up to the reader to define their own impartial spectator to help guide decisions under uncertainty.
Adam Smith, of course, did not have the benefit of modern neurobiology or behavioral psychology to inform his deductions. His deductive intuitions have stood the test of time.
Thus, while time has not validated Samuelson's theories on rationality, time has validated Smith's theories on moral sentiment, the impartial spectator, and the invisible hand. It is interesting, perhaps consistent with the Lindy Effect, that Smith's work was published almost 200 years before Samuelson's.
[iii] Hulett, Changing our mind and using Bayesian Inference in our day-to-day life, The Curiosity Vine, 2023